The application of radiation to an individual is commonplace in a number of industries, such as the medical industry, where radiation is used in imaging applications and therapy applications, and the security industry, where radiation is used to perform inspection processes. Regardless of the industry, whenever radiation is applied to a living subject, a balance is sought between the ability to achieve the best results of the process utilizing the radiation, which typically leans toward increased radiation dosage, and the safety of the subject, which typically leans toward decreased radiation dosage.
To this end, a constant difficulty that arises in radiotherapy treatment planning is the patient-specific tradeoff between providing an appropriate radiation dose to a tumor and keeping the healthy tissue dose low. This is traditionally handled by forming a joint objective function that includes an objective that rewards high, uniform dose to the tumor, as well as separate objectives that penalize dose to various healthy organs or tissue. The problem with this approach is that a good set of relative weighting factors on the different objectives is not a priori known, and must be found by the treatment planner using a, time-consuming, iterative, process, often based at least partially on trial and error. Furthermore, a set of weights found for one patient will likely not work well for another patient. Indeed, it has been shown that for a concave tumor phantom and a nearby critical structure, the geometry parameterized by the separation distance and the relative weights that give comparable plans are different for each instance.
Therefore, it would be desirable to have a system and method that allows treatment planners and physicians to understand the tradeoffs for individual patients, while simultaneously avoiding the time-consuming human-iteration loop of searching for a good set of objective function weights.